摘要
针对现有碳排放预测方法精度较低、稳定性较差、数据难以获取的问题,提出一种基于电力数据的碳排放组合预测方法,分别建立改进BP神经网络、随机森林回归、Elman神经网络预测模型,并对这3种模型进行组合优化,建立基于电力数据的碳排放组合预测模型。对湖南省进行电-碳预测仿真,合理选取电力输入变量,对比分析所提组合预测模型与单项预测、其他组合预测模型。研究结果表明,电力数据能有效应用于碳排放预测,说明所提组合预测模型有着较高的精度,能有效应用于预测和减少碳排放量。
Aiming at the problems of low accuracy,poor stability,and difficulty in obtaining data in existing carbon emission forecasting methods,this paper proposes a carbon emission combination forecasting method based on power data.The improved BP neural network,random forest regression and Elman neural network forecasting models are established respectively,and the three models are combined and optimized to establish a carbon emission combination forecasting model based on power data.Through the simulation of power-carbon forecasting in Hunan Province,the power input variables are selected reasonably,and the combination forecasting model proposed in this paper is compared and analyzed with single forecasting and other combination forecasting models.The research result shows that power data can be effectively applied to carbon emission forecasting.Compared with the individual forecasting models and the other two combination forecasting models,the combination forecasting model proposed in this paper has higher accuracy and helps predict and reduce carbon emissions.
作者
文博
李家熙
文明
张欣杨
许加柱
WEN Bo;LI Jiaxi;WEN Ming;ZHANG Xinyang;XU Jiazhu(State Grid Hunan Electric Power Company Limited Economic and Technical Research Institute,Changsha 410007,China;School of Electrical Engineering,Hunan University,Changsha 410082,China)
出处
《湖南电力》
2024年第2期134-140,共7页
Hunan Electric Power
基金
国网湖南省电力有限公司科技项目(5216A8220001)
湖南省重点实验室项目(2019TP1053)。
关键词
碳排放预测
改进BP神经网络
随机森林回归
ELMAN神经网络
组合预测
carbon emission forecasting
improved BP neural network
random forest regression
Elman neural network
combination forecasting
